Mastering the Grid: Reinforcement Learning for Adaptive Traffic Control

Thu, 15 Oct 2020 00:00:00 +0000

🤖 Smarter Intersections, Smoother Cities

In sprawling metropolises like Tehran, drivers spend nearly 24% of their travel time idling at intersections. Traditional fixed-time traffic lights simply cannot adapt to the chaotic, stochastic nature of urban traffic flow. To solve this, our research deploys Reinforcement Learning (RL)—an advanced branch of Artificial Intelligence—to create traffic signals that learn, adapt, and optimize the network in real-time.

Network Optimization on Mohammad Movahedi

Mastering the Grid: Reinforcement Learning for Adaptive Traffic Control

🤖 Smarter Intersections, Smoother Cities